Asset Management, GIS and LiDAR Projects

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Month: March 2010

When we were at the ILMF conference, I had someone stop by and ask us about fusing Mobile LiDAR and Bathymetric data. So, I asked him for an XYZ file and we made the import into EarthView. The data looked great calibration-wise and the data sets seemed to line up pretty well from a high level. His main concern was related to data editing portion of the project.

As with any LiDAR project – it is pretty easy to collect and calibrate the data, but making it useful for analysis is the hard part! The Bathy portion of this project had a lot of noise in the point cloud and required some re-classification for sure as shown in the graphic below. The Blue data is the Mobile data and the Green data is the Bathy data – each is colored to its corresponding point cloud.

Mobile and Bathymetric Data by Line

The Floating blue points are mostly the water surface, but some of it is floating above the surface and needs to edited out of the point cloud. We handle this by using our editing tools to re-class those points into the “Water” class so that it has a home in the point cloud. The next graphic shows how we can re-class those points with editing and the resulting “Hole” in the point cloud where the Water class has been turned off.

Mobile/Bathy Water Edits

Please note that all of the data hasn’t been edited for this demo, just a subset to show the editing tools.

Here is a profile view of some boats parked in their slips – this shows above-ground features and underwater features simultaneously.

Boat Slips Above and Below Water Line

Once again, this is all “cool” in terms of pictures, but there is a lot of noise in the data that needs to be hand-edited before a true surface can be created with the data. We’re working this data as we speak and I’ll post more about it when we’re finished editing!

We have another pretty neat mobile and airborne LiDAR project here in Orlando and the data has just started to materialize. Over the next couple of weeks, I’ll be posting the data and discussing some of the neat things about it all. I’ll first start by laying out the project and what we’re trying to do with it and then start discussing the results as we get into the analysis portion of the project.

The project is located in Orlando, FL down near the southern junction of I-4 and SR 417. We are supporting a resurfacing project that is about 5 miles in length. The goal of the project is to see if we can save the design engineers time and money by using mobile LiDAR to collect the corridor and give them an Engineering-grade model of the existing paved surface. They will use this information to design the resurfacing project and hopefully save on materials in the field by using an accurate model of the existing conditions.

Traditionally, this information was collected through the use of Low-Altitude Mapping and Photogrammetry (LAMP) or by surveying cross-sections along the project. Both of these technologies work and are proven to be accurate, but nothing can beat using a digital terrain model built from millions of points, right? That is what we’re going to use and we are just getting some preliminary data from our partners in this project, Riegl Corporation.

We have a sneak peek of their new scanners, the VMX-250. This scanner is pretty amazing and has caused us to re-write our software to handle the large amounts of data it generates. The graphic below shows an ulfiltered data set of a portion of the project.

417 Project by Drive Line

The colors represent each drive line captured in each direction. There are a total of 2 drive lines here, each drive line is collecting data from 2 scanners. As you can see, there is a bunch of “junk” in there, but if you look in 3D mode, you can see that the drive lines are calibrated pretty well.

417 by Drive Line in 3D

All of the data above was collected in 2 passes with 2 scanners which is pretty amazing. I have all 4 loaded up and the data size is over 10Gb for about a 1-mile portion of the project. So, as you can tell, there is a ton of data to review, edit and mine for this project!

417 by Intensity and Profile

I’ll end this post with the graphic above showing a cross-section of the road and a view of it by intensity. We’ll be working with this data over the next few weeks and as we get some results, I’ll post them here!

So, I have been working with the guys to keep my feet wet with LiDAR data editing so that I understand more about what it takes to prepare the LiDAR surface for delivery to the client or for an ortho surface. I got my share of data and headed off to edit my surface…

Sounds pretty easy, right? Well, it actually is as long as you know what to look for. Our data for this project had a lot of low points in it – due mostly to the fact that we are shooting down stormwater grates in neighborhoods. This creates low points in the data that is not indicative of the true terrain. We usually filter these out using our filtering algorithms, but sometimes these points still exist in the data and need to be edited out.

The first way to identify a low point is to create a TIN of the surface. If there are low points, the TIN will be dragged down by the surface and there will be a gaping hole in the surface. Another way to identify these holes is to look at the color palette of the scene and if it does not have the usual distribution of colors – Red to Purple – there is a low point somewhere in the scene.

Low Point in TIN Surface

We can also see the low point using the “Profile” view – it can be seen below the surface.

Low Points Below the Filtered Surface

These points can be re-classified and removed from the Ground Classification and placed into the “Low Point / Noise” Classification and then the surface is modified. Note the better distribution of the color palette for the scene…

Resulting TIN Surface

Finally, the resulting profile shows the points reclassified to the correct classification. Repeat for each tile until complete!

We just completed a project for a private landfill here in FL to help settle a contractor dispute about how much dirt was moved/removed from a retention pond. The problem stemmed from the fact that the design engineer estimated the volume as one amount of cubic yards and the earthworks guys sent a bill for twice that amount!

Project Site

We thought it would be easy by collecting it with airborne LiDAR as part of our flight testing, but then realized that the area in question was a pond that was under water! So, back to the drawing board…

Back in my RCID/Disney days, I worked with some smart people and we learned how to integrate GPS and Bathymetric sensors to map the Hydrilla in their lakes. We also gathered some useful Bathymetric data that could be used to determine target concentrations of herbicides based on a specific dilution factor. The most important part of that equation was knowing the amount of water in the lake and it was a math formula from there on forward. Divide the volume by the target concentration level and you had the amount of herbicide needed to make the brew.

GPS Track of Bathymetric Data

So, we went old school and used our RTK rover to supply a GPS location and the Bathymetric sensor to grab the Z (depth) values for the lake in question. The collection took about an hour and we had a processed and calibrated bathymetric surface before leaving the project site. From there, we integrated the bathy data with the airborne LiDAR to get a continuous representation of the underwater surface.

Solid Rendering of Bathymetric Data with Airborne LiDAR

There was a small discrepancy between the water elevation on the day of airborne collection and the bathy collection. This was handled by surveying the water elevation on the day of the bathy collection and then adjusting all of the depths to this elevation (corrected for the transducer offset which was about 0.1 foot). This gave us the correct elevations relative to the airborne LiDAR data set.

Profile of Bathymetric Data Showing an "Empty" Lake

We determined that the volume of dirt removed was the same as the yield as determined by the design engineer. It turns out that the contractor might have to come to the table to prove that they moved more material then the design engineer predicted and we confirmed with this cool project!